"""simple docstring"""


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[str]    				=			['''image_processor''', '''tokenizer''']
				A__ :  Dict    				=			'''LayoutLMv3ImageProcessor'''
				A__ :  Tuple    				=			('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					**_lowerCamelCase ):
										lowerCamelCase__   =None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																    " instead."      ,					_lowerCamelCase      ,					)
																lowerCamelCase__   =kwargs.pop("feature_extractor" )

										lowerCamelCase__   =image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError("You need to specify an `image_processor`." )
										if tokenizer is None:
																raise ValueError("You need to specify a `tokenizer`." )

										super().__init__(_lowerCamelCase      ,					_lowerCamelCase )

				def __call__( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = True      ,					_lowerCamelCase = False      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = 0      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = False      ,					_lowerCamelCase = False      ,					_lowerCamelCase = False      ,					_lowerCamelCase = False      ,					_lowerCamelCase = True      ,					_lowerCamelCase = None      ,					**_lowerCamelCase      ,					):
										# verify input
										if self.image_processor.apply_ocr and (boxes is not None):
																raise ValueError(
																    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." )

										if self.image_processor.apply_ocr and (word_labels is not None):
																raise ValueError(
																    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )

										# first, apply the image processor
										lowerCamelCase__   =self.image_processor(images=_lowerCamelCase      ,					return_tensors=_lowerCamelCase )

										# second, apply the tokenizer
										if text is not None and self.image_processor.apply_ocr and text_pair is None:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =[text]  # add batch dimension (as the image processor always adds a batch dimension)
																lowerCamelCase__   =features["words"]

										lowerCamelCase__   =self.tokenizer(
										    text=text if text is not None else features["words"]      ,					text_pair=text_pair if text_pair is not None else None      ,					boxes=boxes if boxes is not None else features["boxes"]      ,					word_labels=_lowerCamelCase      ,					add_special_tokens=_lowerCamelCase      ,					padding=_lowerCamelCase      ,					truncation=_lowerCamelCase      ,					max_length=_lowerCamelCase      ,					stride=_lowerCamelCase      ,					pad_to_multiple_of=_lowerCamelCase      ,					return_token_type_ids=_lowerCamelCase      ,					return_attention_mask=_lowerCamelCase      ,					return_overflowing_tokens=_lowerCamelCase      ,					return_special_tokens_mask=_lowerCamelCase      ,					return_offsets_mapping=_lowerCamelCase      ,					return_length=_lowerCamelCase      ,					verbose=_lowerCamelCase      ,					return_tensors=_lowerCamelCase      ,					**_lowerCamelCase      ,					)

										# add pixel values
										lowerCamelCase__   =features.pop("pixel_values" )
										if return_overflowing_tokens is True:
																lowerCamelCase__   =self.get_overflowing_images(_lowerCamelCase      ,					encoded_inputs["overflow_to_sample_mapping"] )
										lowerCamelCase__   =images

										return encoded_inputs

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										lowerCamelCase__   =[]
										for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx] )

										if len(_lowerCamelCase ) != len(_lowerCamelCase ):
																raise ValueError(
																    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																    F''' {len(_lowerCamelCase )} and {len(_lowerCamelCase )}''' )

										return images_with_overflow

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.batch_decode(*_lowerCamelCase      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.decode(*_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return ["input_ids", "bbox", "attention_mask", "pixel_values"]

				@property
				def 						_a      ( self ):
										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,					_lowerCamelCase      ,					)
										return self.image_processor_class






				@property
				def 						_a      ( self ):
										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,					_lowerCamelCase      ,					)
										return self.image_processor



"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				={
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a				=[
	    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MgpstrModel',
	    'MgpstrPreTrainedModel',
	    'MgpstrForSceneTextRecognition',
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	a				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


